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ten_Run_GA_Attacker_Q2.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Aug 2 19:31:41 2019
@author: Gerry Dozier
Modified by: Janzaib Masood
Submission: Homework1 --- Question2
"""
import os
import random
import sys
import math
import matplotlib
import matplotlib.pyplot as plt
if sys.platform == "darwin": # Only for osX
matplotlib.use('TkAgg')
matplotlib.rcParams.update({'font.size': 17})
from mpl_toolkits.mplot3d import Axes3D
import ipdb as ipdb
import numpy as np
#
# A Simple Steady-State, Real-Coded Genetic Algorithm
#
class anIndividual:
def __init__(self, specified_chromosome_length):
self.chromosome = []
self.fitness = 0
self.chromosome_length = specified_chromosome_length
def randomly_generate(self,lb, ub):
for i in range(self.chromosome_length):
self.chromosome.append(random.uniform(lb, ub))
self.fitness = 0
def calculate_fitness(self):
x2y2 = self.chromosome[0]**2 + self.chromosome[1]**2
self.fitness = 0.5 + (math.sin(math.sqrt(x2y2))**2 - 0.5) / (1+0.001*x2y2)**2
def print_individual(self, i):
print("Chromosome "+str(i) +": " + str(self.chromosome) + " Fitness: " + str(self.fitness))
class aSimpleExploratoryAttacker:
def __init__(self, population_size, chromosome_length, mutation_rate, lb, ub):
if (population_size < 2):
print("Error: Population Size must be greater than 2")
sys.exit()
self.population_size = population_size
self.chromosome_length = chromosome_length
self.mutation_amt = mutation_rate
self.lb = lb
self.ub = ub
self.mutation_amt = mutation_rate * (ub - lb)
self.population = []
self.hacker_tracker_x = []
self.hacker_tracker_y = []
self.hacker_tracker_z = []
def generate_initial_population(self):
for i in range(self.population_size):
individual = anIndividual(self.chromosome_length)
individual.randomly_generate(self.lb,self.ub)
individual.calculate_fitness()
self.hacker_tracker_x.append(individual.chromosome[0])
self.hacker_tracker_y.append(individual.chromosome[1])
self.hacker_tracker_z.append(individual.fitness)
self.population.append(individual)
def get_worst_fit_individual(self):
worst_fitness = 999999999.0 # For Maximization
worst_individual = -1
for i in range(self.population_size):
if (self.population[i].fitness < worst_fitness):
worst_fitness = self.population[i].fitness
worst_individual = i
return worst_individual
def get_best_fitness(self):
best_fitness = -99999999999.0
best_individual = -1
for i in range(self.population_size):
if self.population[i].fitness > best_fitness:
best_fitness = self.population[i].fitness
best_individual = i
return best_fitness
def evolutionary_cycle(self):
mom = random.randint(0,self.population_size-1)
dad = random.randint(0,self.population_size-1)
kid = self.get_worst_fit_individual()
for j in range(self.chromosome_length):
self.population[kid].chromosome[j] = random.uniform(self.population[mom].chromosome[j],self.population[dad].chromosome[j])
self.population[kid].chromosome[j] += self.mutation_amt * random.gauss(0,1.0)
if self.population[kid].chromosome[j] > self.ub:
self.population[kid].chromosome[j] = self.ub
if self.population[kid].chromosome[j] < self.lb:
self.population[kid].chromosome[j] = self.lb
self.population[kid].calculate_fitness()
self.hacker_tracker_x.append(self.population[kid].chromosome[0])
self.hacker_tracker_y.append(self.population[kid].chromosome[1])
self.hacker_tracker_z.append(self.population[kid].fitness)
def print_population(self):
for i in range(self.population_size):
self.population[i].print_individual(i)
def print_best_max_fitness(self):
best_fitness = -999999999.0 # For Maximization
best_individual = -1
for i in range(self.population_size):
if self.population[i].fitness > best_fitness:
best_fitness = self.population[i].fitness
best_individual = i
print("Best Indvidual: ",str(best_individual)," ", self.population[best_individual].chromosome, " Fitness: ", str(best_fitness))
def plot_evolved_candidate_solutions(self):
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1,projection='3d')
ax1.scatter(self.hacker_tracker_x,self.hacker_tracker_y,self.hacker_tracker_z)
plt.title("Evolved Candidate Solutions")
ax1.set_xlim3d(-100.0,100.0)
ax1.set_ylim3d(-100.0,100.0)
ax1.set_zlim3d(0.2,1.0)
plt.show()
ChromLength = 2
ub = 100.0
lb = -100.0
MaxEvaluations = 4000
plot = 0
PopSize = 20
mu_amt = 0.00675
fitList = []
evalList = []
for run in range(10):
simple_exploratory_attacker = aSimpleExploratoryAttacker(PopSize,ChromLength,mu_amt,lb,ub)
simple_exploratory_attacker.generate_initial_population()
#simple_exploratory_attacker.print_population()
for i in range(MaxEvaluations-PopSize+1):
simple_exploratory_attacker.evolutionary_cycle()
#if (i % PopSize == 0):
# if (plot == 1):
# simple_exploratory_attacker.plot_evolved_candidate_solutions()
# print("At Iteration: " + str(i))
# simple_exploratory_attacker.print_population()
if (simple_exploratory_attacker.get_best_fitness() >= 0.99754):
break
#print("\nFinal Population\n")
#simple_exploratory_attacker.print_population()
simple_exploratory_attacker.print_best_max_fitness()
fitList.append(simple_exploratory_attacker.get_best_fitness())
evalList.append(PopSize+i)
print("Function Evaluations: " + str(PopSize+i))
#simple_exploratory_attacker.plot_evolved_candidate_solutions()
del simple_exploratory_attacker
plt.figure(figsize=(20,10))
plt.subplot(121)
plt.scatter(np.array(list(range(10))), np.array(fitList))
plt.title("Ten Fits (P = "+str(PopSize)+", mu = "+str(mu_amt))
plt.xlabel("Runs")
plt.ylabel("Fitness Score")
plt.xlim((0,10))
plt.ylim((0,1.1))
plt.grid(True)
plt.subplot(122)
plt.scatter(np.array(list(range(10))), np.array(evalList))
plt.title("Func Evals (P = "+str(PopSize)+", mu = "+str(mu_amt))
plt.xlabel("Runs")
plt.ylabel("Function Evaluations")
plt.xlim((0,10))
plt.ylim((0,MaxEvaluations+100))
plt.grid(True)
#plt.show()
print("Average # of function evaluations = ", sum(evalList)/len(evalList))